Medical imaging technologies, such as magnetic resonance imaging (MRI) or computed tomography (CT), can be used to study perfusion in vascular networks of tissues and organs. The perfusion may be characterized to detect and assess cardiovascular diseases, tumors, and other medical conditions, as well as to monitor their treatment. Various analytical approaches to deriving quantitative perfusion parameters from a series of images have been developed and implemented in commercially available software. The accuracy of such a quantitative characterization of blood flow typically depends on both the correct calibration of the imaging apparatus, and the adequacy of the employed perfusion models. Comparative studies have shown that different models and analysis schemes applied to the same set of images may yield considerably different values for perfusion parameters. Accordingly, there is a need to develop an improved standard against which both the imaging apparatus and the analysis software can be calibrated.
Calibration of MRI and other tomographic imaging systems is typically accomplished using a “phantom,” for example, an artificial object of known size and composition that is imaged to test, adjust, and/or monitor the system's homogeneity, imaging performance, and/or orientation. A phantom may be a simple fluid-filled container or bottle, or may include structures such as packed beds or assemblies of macroscale tubes. One limitation of such phantoms is their inability to replicate perfusion in the microcirculation. Previous attempts to overcome this limitation, such as utilizing excised animal tissues, typically lacked quantifiable flow patterns and were generally not robust or stable over time. Accordingly, there is also a need for improved phantoms for non-invasive perfusion imaging applications.